01938cam a22002417 4500001000700000003000500007005001700012008004100029100001400070245014100084260006600225490004100291500001600332520093800348530006101286538007201347538003601419700002401455710004201479830007601521856003801597856006101635c12994NBER20171213213917.0171213s2015 mau||||fs|||| 000 0 eng d1 aWu, Lynn.14aThe Future of Predictionh[electronic resource]:bHow Google Searches Foreshadow Housing Prices and Sales /cLynn Wu, Erik Brynjolfsson. aCambridge, Mass.bNational Bureau of Economic Researchc2015.1 aNBER book chapter seriesvno. c12994 aApril 2015.3 aWe demonstrate how data from search engines such as Google provide an accurate but simple way to predict future business activities. Applying our methodology to predict housing market trends, we find that a housing search index is strongly predictive of future housing market sales and prices. For state-level predictions in the United States, the use of search data produces out-of-sample predictions with a smaller mean absolute error than the baseline model that uses conventional data but lacks search data. Furthermore, we find that our simple model of using search frequencies beat the predictions published by experts from the National Association of Realtors by 23.6% for future US home sales. We also demonstrate how these data can be used in other markets, such as home appliance sales. This type of "nanoeconomic" data can transform prediction in numerous markets, thereby improving business and consumer decision making. aHardcopy version available to institutional subscribers. aSystem requirements: Adobe [Acrobat] Reader required for PDF files. aMode of access: World Wide Web.1 aBrynjolfsson, Erik.2 aNational Bureau of Economic Research. 0aBook Chapter Series (National Bureau of Economic Research)vno. c12994.4 uhttp://www.nber.org/papers/c1299441uhttp://dx.doi.org/10.7208/chicago/9780226206981.003.0003